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    <abstract>Pre-training large language models faces significant memory challenges due to the large size of model weights.We propose STaged parameter-Efficient Pre-training (STEP), which combines ideas from parameter-efficient tuning and staged training. We conduct experiments on pre-training models of various sizes and demonstrate that STEP can achieve up to a 40.4% reduction in maximum memory requirement compared to vanilla pre-training while maintaining comparable performance.</abstract>
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%0 Conference Proceedings
%T STEP: Staged Parameter-Efficient Pre-training for Large Language Models
%A Yano, Kazuki
%A Ito, Takumi
%A Suzuki, Jun
%Y Fu, Xiyan
%Y Fleisig, Eve
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F yano-etal-2024-step
%X Pre-training large language models faces significant memory challenges due to the large size of model weights.We propose STaged parameter-Efficient Pre-training (STEP), which combines ideas from parameter-efficient tuning and staged training. We conduct experiments on pre-training models of various sizes and demonstrate that STEP can achieve up to a 40.4% reduction in maximum memory requirement compared to vanilla pre-training while maintaining comparable performance.
%U https://aclanthology.org/2024.acl-srw.50
%P 607-614

Markdown (Informal)

[STEP: Staged Parameter-Efficient Pre-training for Large Language Models](https://aclanthology.org/2024.acl-srw.50) (Yano et al., ACL 2024)

• STEP: Staged Parameter-Efficient Pre-training for Large Language Models (Yano et al., ACL 2024)

ACL

• Kazuki Yano, Takumi Ito, and Jun Suzuki. 2024. STEP: Staged Parameter-Efficient Pre-training for Large Language Models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop), pages 607–614, Bangkok, Thailand. Association for Computational Linguistics.